1. Depth-First Search
CSE 2011
Winter 2011
28 April 2019

2. Depth-First Search (DFS)
DFS is another popular graph search strategy
Idea is similar to pre-order traversal (visit node, then visit children recursively)
DFS will continue to visit neighbors in a recursive pattern
Whenever we visit v from u, we recursively visit all unvisited neighbors of v. Then we backtrack (return) to u.

3. DFS Traversal Example

4. DFS Algorithm Flag all vertices as not visited
Flag v as visited; print v;
print v;
For unvisited neighbors, call RDFS(w) recursively
We can also record the paths using prev[ ].
Where do we insert the code for prev[ ]?

5. Example Adjacency List Visited Table (T/F) 8 2 9 1 7 3 6 4 5 Pred1 2 3 4 5 6 7 8 9 F -
source
Pred
Initialize visited
table (all False)
Initialize Pred to -1

6. Adjacency List Visited Table (T/F) 8 2 9 1 7 3 6 4 5 Pred1 2 3 4 5 6 7 8 9 F T -
source
Pred
Mark 2 as visited
RDFS( 2 )
Now visit RDFS(8)

7. 2 is already visited, so visit RDFS(0)
Adjacency List
Visited Table (T/F) 2 4 3 5 1 7 6 9 8 1 2 3 4 5 6 7 8 9 F T - 2
source
Pred
Mark 8 as visited
mark Pred[8]
Recursive calls
RDFS( 2 )
RDFS(8)
2 is already visited, so visit RDFS(0)

8. RDFS(0) -> no unvisited neighbors, return to call RDFS(8)
Adjacency List
Visited Table (T/F) 2 4 3 5 1 7 6 9 8 1 2 3 4 5 6 7 8 9 T F 8 - 2
source
Pred
Mark 0 as visited
Mark Pred[0]
Recursive calls
RDFS( 2 )
RDFS(8)
RDFS(0) -> no unvisited neighbors, return
to call RDFS(8)

9. Back to 8 Adjacency List Visited Table (T/F) 8 2 9 1 7 3 6 4 5 Pred1 2 3 4 5 6 7 8 9 T F 8 - 2
source
Pred
Recursive calls
RDFS( 2 )
RDFS(8)
Now visit 9 -> RDFS(9)

10. Adjacency List Visited Table (T/F) 8 2 9 1 7 3 6 4 5 Pred1 2 3 4 5 6 7 8 9 T F 8 - 2
source
Pred
Mark 9 as visited
Mark Pred[9]
Recursive calls
RDFS( 2 )
RDFS(8)
RDFS(9)
-> visit 1, RDFS(1)

11. Adjacency List Visited Table (T/F) 8 2 9 1 7 3 6 4 5 Pred1 2 3 4 5 6 7 8 9 T F 8 9 - 2
source
Pred
Mark 1 as visited
Mark Pred[1]
Recursive calls
RDFS( 2 )
RDFS(8)
RDFS(9)
RDFS(1)
visit RDFS(3)

12. Adjacency List Visited Table (T/F) 8 2 9 1 7 3 6 4 5 Pred1 2 3 4 5 6 7 8 9 T F 8 9 - 1 2
source
Pred
Mark 3 as visited
Mark Pred[3]
Recursive calls
RDFS( 2 )
RDFS(8)
RDFS(9)
RDFS(1)
RDFS(3)
visit RDFS(4)

13. RDFS(4)  STOP all of 4’s neighbors have been visited
Adjacency List
Visited Table (T/F) 2 4 3 5 1 7 6 9 8 1 2 3 4 5 6 7 8 9 T F 8 9 - 1 3 2
source
Pred
RDFS( 2 )
RDFS(8)
RDFS(9)
RDFS(1)
RDFS(3)
RDFS(4)  STOP all of 4’s neighbors have been visited
return back to call RDFS(3)
Mark 4 as visited
Mark Pred[4]
Recursive calls

14. Adjacency List Visited Table (T/F) 8 2 9 1 7 3 6 4 5 Back to 3 Pred1 2 3 4 5 6 7 8 9 T F 8 9 - 1 3 2
source
Back to 3
Pred
RDFS( 2 )
RDFS(8)
RDFS(9)
RDFS(1)
RDFS(3)
visit 5 -> RDFS(5)
Recursive calls

15. 3 is already visited, so visit 6 -> RDFS(6) Mark 5 as visited
Adjacency List
Visited Table (T/F) 2 4 3 5 1 7 6 9 8 1 2 3 4 5 6 7 8 9 T F 8 9 - 1 3 2
source
Pred
RDFS( 2 )
RDFS(8)
RDFS(9)
RDFS(1)
RDFS(3)
RDFS(5)
3 is already visited, so visit 6 -> RDFS(6)
Mark 5 as visited
Mark Pred[5]
Recursive calls

16. Adjacency List Visited Table (T/F) 8 2 9 1 7 3 6 4 5 Pred RDFS( 2 )1 2 3 4 5 6 7 8 9 T F 8 9 - 1 3 5 2
source
Pred
RDFS( 2 )
RDFS(8)
RDFS(9)
RDFS(1)
RDFS(3)
RDFS(5)
RDFS(6)
visit 7 -> RDFS(7)
Mark 6 as visited
Mark Pred[6]
Recursive calls

17. RDFS(7) -> Stop no more unvisited neighbors Mark 7 as visited
Adjacency List
Visited Table (T/F) 2 4 3 5 1 7 6 9 8 1 2 3 4 5 6 7 8 9 T 8 9 - 1 3 5 6 2
source
Pred
RDFS( 2 )
RDFS(8)
RDFS(9)
RDFS(1)
RDFS(3)
RDFS(5)
RDFS(6)
RDFS(7) -> Stop no more unvisited neighbors
Mark 7 as visited
Mark Pred[7]
Recursive calls

18. Adjacency List Visited Table (T/F) 8 2 9 1 7 3 6 4 5 Pred RDFS( 2 )1 2 3 4 5 6 7 8 9 T 8 9 - 1 3 5 6 2
source
Pred
RDFS( 2 )
RDFS(8)
RDFS(9)
RDFS(1)
RDFS(3)
RDFS(5)
RDFS(6) -> Stop
Recursive calls

19. Adjacency List Visited Table (T/F) 8 2 9 1 7 3 6 4 5 Pred RDFS( 2 )1 2 3 4 5 6 7 8 9 T 8 9 - 1 3 5 6 2
source
Pred
RDFS( 2 )
RDFS(8)
RDFS(9)
RDFS(1)
RDFS(3)
RDFS(5) -> Stop
Recursive calls

20. Adjacency List Visited Table (T/F) 8 2 9 1 7 3 6 4 5 Pred RDFS( 2 )1 2 3 4 5 6 7 8 9 T 8 9 - 1 3 5 6 2
source
Pred
RDFS( 2 )
RDFS(8)
RDFS(9)
RDFS(1)
RDFS(3) -> Stop
Recursive calls

21. Adjacency List Visited Table (T/F) 8 2 9 1 7 3 6 4 5 Pred RDFS( 2 )1 2 3 4 5 6 7 8 9 T 8 9 - 1 3 5 6 2
source
Pred
RDFS( 2 )
RDFS(8)
RDFS(9)
RDFS(1) -> Stop
Recursive calls

22. Adjacency List Visited Table (T/F) 8 2 9 1 7 3 6 4 5 Pred RDFS( 2 )1 2 3 4 5 6 7 8 9 T 8 9 - 1 3 5 6 2
source
Pred
RDFS( 2 )
RDFS(8)
RDFS(9) -> Stop
Recursive calls

23. Adjacency List Visited Table (T/F) 8 2 9 1 7 3 6 4 5 Pred RDFS( 2 )1 2 3 4 5 6 7 8 9 T 8 9 - 1 3 5 6 2
source
Pred
RDFS( 2 )
RDFS(8) -> Stop
Recursive calls

24. Example Finished Recursive calls finished Adjacency List
Visited Table (T/F) 2 4 3 5 1 7 6 9 8 1 2 3 4 5 6 7 8 9 T 8 9 - 1 3 5 6 2
source
Pred
RDFS( 2 ) -> Stop
Recursive calls finished

25. Time Complexity of DFS We never visited a vertex more than once.
We had to examine the adjacency lists of all vertices.
Σvertex v degree(v) = 2E
So, the running time of DFS is proportional to the number of edges and number of vertices (same as BFS)
O(V + E)

26. Enhanced DFS Algorithm
What if a graph is not connected (strongly connected)?
Use an enhanced version of DFS, which is similar to the enhanced BFS algorithm.
DFSearch( G ) {
i = 1; // component number
for every vertex v
flag[v] = false;
if ( flag[v] == false ) {
print ( “Component ” + i++ );
RDFS( v ); }

27. Applications of DFS Is there a path from source s to a vertex v?
Is an undirected graph connected?
Is a directed graph strongly connected?
To output the contents (e.g., the vertices) of a graph
To find the connected components of a graph
To find out if a graph contains cycles and report cycles.
To construct a DSF tree/forest from a graph

28. DFS Path Tracking Adjacency List Visited Table (T/F) 8 2 9 1 7 3 6 4 5
Adjacency List
Visited Table (T/F) 1 2 3 4 5 6 7 8 9 T 8 9 - 1 3 5 6 2
source
DFS find out path too
Pred
Try some examples.
Path(0) ->
Path(6) ->
Path(7) ->

29. Next time …
Applications of BFS and DFS
Review